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Analysis of Hourly Values

TestTitre

L’équation cubique de Gausse

Se résoud en développant en \(e^{-x}\)

\[ \int_0^\infty \frac{x^3}{e^x-1}\,dx = \frac{\pi^4}{15} \]

()\[ w_{t+1} = (1 + r_{t+1}) s(w_t) + y_{t+1} \]

()\[w_{t+1} = (1 + r_{t+1}) s(w_t) + y_{t+1}\]

• A link to an equation directive: ()

• A link to a dollar math block: ()

import plotFuncAndClass as hm
import pandas as pd
from ipywidgets import interact, interactive, fixed, interact_manual, IntSlider
# Standard plotly imports
import chart_studio.plotly as py
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
# Using plotly + cufflinks in offline mode
import cufflinks as cf
cf.go_offline(connected=False)
init_notebook_mode(connected=False)

listLat=['\Delta t','DNI', 'T_{amb}', '\dot{m}_{Demand}', 'T_{demand}', '\dot{m}_{SF}', 'T_{SF}', '\dot{m}_{aux}',
    '\dot{q}_{boiler}', '\dot{q}_{abs}','M_{storage}', 'T_{storage}', '\dot{m}_{Storage -> boiler}', '\dot{m}_{SF->Boiler}', '\dot{m}_{SF->Storage}']
varDescription=['Hour','Direct Normal Irradiance', 'Ambient Temperature', 'Demand Mass Flow Rate', 'Temperature Demand', 
                'Mass flow rate from the Solar Field', 'Temperature of the Solar Field', 'Auxiliar Mass flow rate', 
               'Heat From The Boiler', 'Heat absorbed by the receiver','Mass in the Storage', 'Temperature of the storage', 'Mass flow rate from the Storage to the Boiler',
               'Mass flow rate from the Solar Field to the Boiler', 'Mass flow rate from the Solar Field to the Storage']
df = pd.read_csv('data/CoSim.csv') #read a specific csv file
dfMILP= pd.read_csv('data/MILP.csv') #read another specific csv file with same fields
dfSM1= pd.read_csv('data/SM1.csv') #read another specific csv file with same fields
dfSM2= pd.read_csv('data/SM2.csv') #read another specific csv file with same fields
tabDataFrames=[df,dfSM1,dfSM2, dfMILP]
tabNameDataFrame=['CoSim','SM1','SM2','MILP','3 CoSim']
nameColumns=list(df.columns)
tabVar=[hm.nameAndUnit(nameColumns[i],nameColumns[i],varDescription[i],'',listLat[i]) for i in range(0,len(nameColumns))]

Daily plotting

df[124:248].iplot(y='mdotSF(kg/s)',secondary_y='DNI(W/m2)',x='hour(h)')

"}, "metadata": {}, "output_type": "display_data"}]}}, "018833b627944eb79d71169517529f37": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {}}, "031cb3d3ee6045b9a5e2b0ff5a688a3e": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {}}, "04dc1551563b435ebdf79458d5a9e481": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_e449a66676874703acddc81c94c21b80", "outputs": [{"ename": "ValueError", "evalue": "\nInvalid value of type receive as an image format specification.\n Received value: html\n\nAn image format must be specified as one of the following string values:\n ['eps', 'jpeg', 'jpg', 'pdf', 'png', 'svg', 'webp']", "output_type": "error", "traceback": ["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 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range(0,3)]].iplot(kind='scatter', asFigure=True,\n\u001b[0;32m 15\u001b[0m title=titleFunc, xTitle='hour (h)', yTitle='$'+var1.lat+'('+var1.unitVar+')$',showlegend=True)\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_image\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"../images/plot1VarDay.html\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 17\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\plotly\\basedatatypes.py\u001b[0m in \u001b[0;36mwrite_image\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 3556\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mio\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpio\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3557\u001b[0m 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{"description_width": ""}}, "07951c548ecd4c41b9d4db1b3a30a332": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "VBoxModel", "state": {"_dom_classes": ["widget-interact"], "children": ["IPY_MODEL_87692291e4364649b38c36b48a99b9d8", "IPY_MODEL_e56bbfce0f1d4897910136b5210c09cc", "IPY_MODEL_cc1d3979a5a34a1a952c04bb82f6286e", "IPY_MODEL_a308449c9d6442e4a46f24e40b0074bb"], "layout": "IPY_MODEL_c6fb6031ae4649d9bfee0ca1b15563d7"}}, "07aee6bd1afd43528511516c67b33ee2": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {}}, "07e5c68195554fc1b94ac572adf39cd5": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {}}, "0d56239f5f2a4d9eb306458ca5d8e81d": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {}}, "0d9e17b0f16542ddb4fecf5204936bb2": {"model_module": 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{"description": "dayStart", "layout": "IPY_MODEL_c2eee49cb06d41449964a841f78a2df9", "max": 361, "style": "IPY_MODEL_1da6e60a22b44e8f99220539348b82ca", "value": 170}}, "557fddfbe7004541b725c598ec3c74d0": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_606c03bc79c64a8da086f0fa7b06a353", "outputs": [{"ename": "ValueError", "evalue": "Invalid value of type received as the include_mathjax argument\n Received value: True\n\ninclude_mathjax may be specified as False, 'cdn', or a string ending with '.js'\n ", "output_type": "error", "traceback": ["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m 254\u001b[0m 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\u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_image\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"../images/plot1VarDay.png\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_html\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"../images/plot1VarDay.html\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minclude_mathjax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\plotly\\basedatatypes.py\u001b[0m in \u001b[0;36mwrite_html\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 3435\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mio\u001b[0m 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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\plotly\\io\\_html.py\u001b[0m in \u001b[0;36mwrite_html\u001b[1;34m(fig, file, config, auto_play, include_plotlyjs, include_mathjax, post_script, full_html, animation_opts, validate, default_width, default_height, auto_open)\u001b[0m\n\u001b[0;32m 506\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 507\u001b[0m \u001b[1;31m# Build HTML string\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 508\u001b[1;33m html_str = to_html(\n\u001b[0m\u001b[0;32m 509\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 510\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\plotly\\io\\_html.py\u001b[0m in \u001b[0;36mto_html\u001b[1;34m(fig, config, auto_play, include_plotlyjs, include_mathjax, post_script, full_html, animation_opts, default_width, default_height, validate)\u001b[0m\n\u001b[0;32m 329\u001b[0m \u001b[0mmathjax_script\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 330\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 331\u001b[1;33m raise ValueError(\n\u001b[0m\u001b[0;32m 332\u001b[0m \"\"\"\\\n\u001b[0;32m 333\u001b[0m \u001b[0mInvalid\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtype\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mtyp\u001b[0m\u001b[1;33m}\u001b[0m \u001b[0mreceived\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mthe\u001b[0m \u001b[0minclude_mathjax\u001b[0m \u001b[0margument\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: Invalid value of type received as the include_mathjax argument\n Received value: 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